{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/np/m42416h166j90krvxmfl40940000gp/T/ipykernel_68696/1163645692.py:3: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  solTchem = (data[1:,:]).astype(np.float)\n"
     ]
    }
   ],
   "source": [
    "data = np.genfromtxt(\"Emissions.dat\", dtype=str)\n",
    "Header = (data[0,:]).tolist()\n",
    "solTchem = (data[1:,:]).astype(np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "campsol = np.loadtxt(\"emission_results.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_indx = Header.index('A')\n",
    "B_indx = Header.index('B')\n",
    "t_indx = Header.index('t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Cells: 1\n",
      "Number of time iterations: 108.0\n"
     ]
    }
   ],
   "source": [
    "niterT, Nvars = np.shape(solTchem)\n",
    "Nsamples = len(np.where(solTchem[:,0]==-1)[0])\n",
    "print('Number of Cells:',Nsamples)\n",
    "print('Number of time iterations:',niterT/Nsamples)\n",
    "solTchem = solTchem.reshape( int(niterT/Nsamples), Nsamples,Nvars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f84d81f6340>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp=0 # cell number 0, camp only saved cell No 1 \n",
    "plt.figure()\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,A_indx], label=\"A\")\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,B_indx], label=\"B\")\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration [mol/m3]')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f84d835ea60>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(campsol[:,0],campsol[:,1],label='CAMP')\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,A_indx],\"*\",label='TChem')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration of A [mol/m3]')\n",
    "plt.legend(loc='best')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f84d8394ca0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(campsol[:,0],campsol[:,3],label='CAMP')\n",
    "plt.plot(solTchem[:,sp,t_indx],solTchem[:,sp,B_indx],\"*\",label='TChem')\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('Concentration of B [mol/m3]')\n",
    "plt.legend(loc='best')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
